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A Conceptual Discussion about R0 of SARS-COV-2 in
Healthcare Settings
Laura Temime, Marie-Paule Gustin, Audrey Duval, Niccolò Buetti, Pascal
Crepey, Didier Guillemot, Rodolphe Thiébaut, Philippe Vanhems, Jean-Ralph
Zahar, David Smith, et al.
To cite this version:
Laura Temime, Marie-Paule Gustin, Audrey Duval, Niccolò Buetti, Pascal Crepey, et al.. A Concep-
tual Discussion about R0 of SARS-COV-2 in Healthcare Settings. Clinical Infectious Diseases, Oxford
University Press (OUP), 2021, pp.ciaa682. �10.1093/cid/ciaa682�. �hal-02859847�
HAL Id: hal-02859847
https://hal.ehesp.fr/hal-02859847
Submitted on 10 Jun 2020
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CopyrightA CONCEPTUAL DISCUSSION ABOUT R0 OF SARS-COV-2 IN HEALTHCARE SETTINGS
Laura TEMIME
MESuRS laboratory, Conservatoire national des arts et métiers, Paris, France; PACRI Unit, Institut
Pasteur, Conservatoire national des arts et métiers, Paris, France.
Marie-Paule GUSTIN
Institute of Pharmaceutic and Biological Sciences, University Claude Bernard Lyon 1, Villeurbanne,
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France; Emerging Pathogens Laboratory-Fondation Mérieux, International Center for Infectiology
Research (CIRI), Inserm U1111, CNRS UMR5308, ENS de Lyon, Lyon, France.
Audrey DUVAL
Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, CESP, Anti-infective evasion and
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pharmacoepidemiology team, Montigny-Le-Bretonneux, France; Institut Pasteur, Epidemiology and
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Modelling of Antibiotic Evasion unit, Paris, France.
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Niccolò BUETTI
INSERM IAME, U1137, Team DesCID, Paris, France.
Pascal CRÉPEY
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UPRES-EA 7449 REPERES « Recherche en Pharmaco-Epidémiologie et Recours aux Soins » – EHESP –
Université de Rennes, Rennes, France.
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Didier GUILLEMOT
Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, CESP, Anti-infective evasion and
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pharmacoepidemiology team, Montigny-Le-Bretonneux, France; Institut Pasteur, Epidemiology and
Modelling of Antibiotic Evasion unit, Paris, France; AP-HP Paris Saclay, Public Health, Medical
Information, Clinical research, Le Kremlin-Bicêtre, France.
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Rodolphe THIÉBAUT
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INSERM U1219 Bordeaux Population Health, Université de Bordeaux, Bordeaux, France; INRIA SISTM
team, Talence, France; Vaccine Research Institute, Créteil, France.
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Philippe VANHEMS
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Emerging Pathogens Laboratory-Fondation Mérieux, International Center for Infectiology Research
(CIRI), Inserm U1111, CNRS UMR5308, ENS de Lyon, Lyon, France; Service d'Hygiène, Epidémiologie
et Prévention, Hospices Civils de Lyon, F-69437, Lyon, France ; Inserm, F-CRIN, Réseau Innovative
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Clinical Research in Vaccinology (I-REIVAC), Paris, France.
Jean-Ralph ZAHAR
IAME, UMR 1137, Université Paris 13, Sorbonne Paris Cité, France; Service de Microbiologie Clinique
et Unité de Contrôle et de Prévention du Risque Infectieux, Groupe Hospitalier Paris Seine Saint-
Denis, AP-HP, Bobigny, France.
David R.M. SMITH
Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, CESP, Anti-infective evasion and
pharmacoepidemiology team, Montigny-Le-Bretonneux, France; Institut Pasteur, Epidemiology and
Modelling of Antibiotic Evasion unit, Paris, France; MESuRS laboratory, Conservatoire national des
arts et métiers, Paris, France.
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of
America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.Lulla OPATOWSKI
Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, CESP, Anti-infective evasion and
pharmacoepidemiology team, Montigny-Le-Bretonneux, France; Institut Pasteur, Epidemiology and
Modelling of Antibiotic Evasion unit, Paris, France.
for the “Modelling COVID-19 in hospitals” REACTinG AVIESAN working group*
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*See acknowledgment section
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Corresponding author:
Laura Temime
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MESuRS laboratory
Conservatoire national des Arts et Métiers
292 rue Saint-Martin – 75141 Paris Cedex 03
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France
Email: laura.temime@lecnam.net
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2ABSTRACT
To date, no specific estimate of R0 for SARS-CoV-2 is available for healthcare settings. Using inter-
individual contact data, we highlight that R0 estimates from the community cannot translate directly
to healthcare settings, with pre-pandemic R0 values ranging 1.3-7.7 in three illustrative healthcare
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institutions. This has implications for nosocomial Covid-19 control.
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Keywords: COVID-19; basic reproduction number; modelling; hospital; transmission
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3In the context of the current Covid-19 pandemic, the basic reproduction number R0 has been
recognized as a key parameter to characterize epidemic risk and predict spread of SARS-CoV-2, the
causative virus of Covid-19 infection [1]. R0 describes the average number of secondary cases
generated by an initial index case in an entirely susceptible population. R0 is determined not only by
the inherent infectiousness of a pathogen, but also environmental conditions, host contact
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behaviours and other factors that influence transmission. Understanding the evolution of the
effective reproduction number Rt, which describes R0 as it varies over time, is also essential for
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epidemiological forecasting and to assess the impact of control strategies [2, 3].
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Over recent months, numerous estimates of R0 for SARS-CoV-2 have been computed through
analysis of reported infections from countries all over the world [2, 4-6], as well as in specific
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subpopulations, such as individuals aboard the Diamond Princess cruise ship [7]. Published estimates
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mostly range from 2-4.
However, to date, no estimates of R0 specific to healthcare settings have been published.
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Healthcare institutions are confronted with several urgent and overlapping challenges linked to
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Covid-19. Acute care facilities face unprecedented demand for beds and resources to accommodate
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Covid-19 patients, particularly in intensive care units in high-prevalence regions. Introduction of
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SARS-CoV-2 to healthcare settings can further result in nosocomial outbreaks, with superspreading
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events already reported in some hospitals [8], as was also observed for SARS-CoV and MERS-CoV. In
addition to risks for patients, whose underlying conditions put them at greater risk of severe
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infection, there is also an important risk of infection among healthcare workers [8].
Contacts between individuals are fundamental to the spread of respiratory pathogens like SARS-CoV-
2, and contact patterns in healthcare settings are highly context-specific. Contacts between patients
and healthcare workers tend to be simultaneously more frequent, longer and more at-risk than
contacts occurring in the community. This could translate to higher R0 values, as underlined in earlier
4work on other coronaviruses, in which R0 was estimated to be much higher in hospitals than in the
community [9].
Here, using detailed individual-level contact pattern data from both the community and three
healthcare institutions in France, we explore how the reproduction number estimated in the
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community may translate to these institutions, and discuss potential consequences for public health.
METHODS
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Under simplifying assumptions, R0 can be estimated as follows:
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where p is the probability of transmission per minute spent in contact, dCtc is the average contact
duration (in minutes), nCtc is the average number of contacts per person per day, and dInf is the
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average duration of infectivity (in days): approximately 10 days for Covid-19 [10].
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Assuming that p and dInf are the same for individuals in the community and in healthcare settings,
we can translate the previous expression into setting-specific R0 values computed as:
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In the community:
pt
In the healthcare settings:
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where superscripts C and H denote values for community and healthcare settings, respectively.
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The healthcare setting-specific reproduction number may then be estimated from the community-
specific reproduction number and the contact pattern characteristics in both settings, as:
5NUMERICAL APPLICATION IN THE FRENCH CONTEXT
Based on detailed inter-individual contact data from France [11], in the community the median
number of inter-individual contacts per person is = 8 contacts/day and the median duration of
these contacts ranges from 15 minutes to 1 hour. For simplicity, in the following we use = 30
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minutes.
The reproduction number for SARS-CoV-2 has been estimated in the French community at values
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ranging from = 2 to 4 [2, 12, 13]. In the following we use = 3.
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These translate to an average transmission risk per minute spent in contact of:
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p = 3 / (8 × 30 × 10) = 0.00125
Table 1 provides estimates of the healthcare setting-specific reproduction number , depending on
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the average number of daily contacts within the healthcare setting , and the actual value of .
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The mean duration of daily contacts within the healthcare setting is assumed to range from 10
to 40 minutes.
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THREE ILLUSTRATIVE EXAMPLES
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As an illustration, we used detailed contact data from three different healthcare settings in France
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during the pre-pandemic period to estimate in the absence of control measures specific to Covid-
19:
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For a 170-bed rehabilitation hospital [14], where = 18 contacts/day and = 34 min,
the pre-pandemic is estimated as
= 0.00125 × 34 × 18 × 10 = 7.65
For an acute-care geriatric unit [15], where the cumulative time spent in contact with others
per individual per day was × = 104 min, the pre-pandemic is estimated as
6= 0.00125 × 104 × 10 = 1.3
For a 100-bed nursing home [16], where the cumulative time spent in contact per individual
and per day was × = 615 min, the pre-pandemic is estimated as
= 0.00125 × 615 × 10 = 7.7
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DISCUSSION
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Estimating R0 has been an important focus of epidemiological work to understand the transmission
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dynamics and pandemic trajectory of SARS-CoV-2. We highlight here that reproduction numbers
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estimated in the community cannot be translated directly to healthcare settings, where inter-
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individual contact patterns are specific to and variable between institutions.
Health care institutions are at high risk of SARS-CoV-2 importation, from admission of infected
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patients or from visitors or healthcare workers infected in the community. Our estimates of
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suggest that, depending on a healthcare facility’s size and structure, the risk of nosocomial spread
may be much higher or lower than in the general population, with values ranging from 0.4 to 13.3
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(Table 1).
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Our results have implications for Covid-19 infection prevention and control. In healthcare settings
with estimated low values of pre-pandemic , it is expected that classical barrier measures –
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reducing p, the probability of transmission per minute of contact – may suffice to prevent a majority
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of cases. On the contrary, in healthcare settings where the estimated pre-pandemic is high, it is
critical to implement additional control measures. These measures could include reducing the
frequency ( ) and duration ( ) of contacts (e.g. through limiting patient-patient contacts by
cancelling social activities and gatherings), limiting patient transfers, or reorganizing human
resources and provisioning of care within the institution.
7It should be underlined that this work’s aim is to present a conceptual discussion about R0 in
healthcare settings. Hence, the elements presented here, and in particular the numerical estimates,
should be interpreted in light of the following over-simplifications.
First, Covid-19 infection was simplified by assuming the same duration of infectivity, irrespective of
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the setting. However, in the community, individuals presenting symptoms may isolate themselves
and stay at home whereas patients of healthcare settings will stay hospitalized. Considering such
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differences would lead to higher estimates of .
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Second, we assumed the same per-minute probability of transmission, irrespective of the setting and
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nature of contacts. However, some hospital contacts, such as those involving close proximity or
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invasive procedures, may pose greater transmission risk than others. Also, a higher concentration of
severe infections, which may shed more virus [17], and the presence of immunosuppressed
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individuals, may entail a higher transmission probability in hospitals, therefore increasing .
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Third, may differ according to individual characteristics, notably for patients vs. healthcare
workers. In addition, some individuals may be super-contactors or super-shedders, with a greater
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probability of generating secondary cases if infected.
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Fourth, contact duration and frequency measured during distinct studies in the community and in
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specific healthcare populations are not necessarily comparable.
Last, our R0 formula assumes random homogenous mixing between individuals in the population.
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However, contact patterns in the general population may depend on age. In addition, hospital
networks are highly clustered due to ward structure and occupational hierarchies. Computing
values using contact information at the ward level and age structure data should facilitate more
accurate estimates. Additionally, our formula makes the assumption that transmission risk increases
linearly with contact duration, which may not be correct, especially for very long contacts. For
8instance, censoring contacts longer than 1 hour in the data from the first example gives an average
contact duration within the facility of 15 min, leading to a lower estimated of 3.37.
In conclusion, pandemic Covid-19 continues to overwhelm healthcare institutions with critically ill
and highly infectious patients, and nosocomial outbreaks pose great risk to patients and healthcare
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workers alike. Understanding how transmission risk varies between community and healthcare
settings, and within and between different healthcare institutions such as hospitals and long-term
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care facilities, is fundamental to better predict risks of nosocomial outbreaks and inform appropriate
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infection control measures.
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9ACKNOWLEDGEMENT
“Modelling COVID-19 in hospitals” REACTinG AVIESAN working group:
Niccolò Buetti, Christian Brun-Buisson, Sylvie Burban, Simon Cauchemez, Guillaume Chelius ,
Anthony Cousien, Pascal Crepey, Vittoria Colizza, Christel Daniel, Aurélien Dinh, Pierre
Frange, Eric Fleury, Antoine Fraboulet, Didier Guillemot, Marie-Paule Gustin, Bich-Tram
Huynh, Lidia Kardas-Sloma, Elsa Kermorvant, Jean Christophe Lucet, Lulla Opatowski, Chiara
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Poletto, Laura Temime, Rodolphe Thiebaut, Sylvie van der Werf, Philippe Vanhems, Linda
Wittkop, Jean-Ralph Zahar.
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FUNDING
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This work was funded in part by the French government through the National Research Agency
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project SPHINX (# 17-CE36-0008-01) and Fondation de France project MOD-COV. DS is also
supported by a Canadian Institutes for Health Research doctoral foreign study award (Funding
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Reference Number 164263).
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Potential Conflicts of Interest
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L.T. reports personal fees from World Health Organization South East Asia, outside the submitted
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work. N.B. reports grants from Swiss National Science Foundation and Bangerter-Rhyner Foundation,
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outside the submitted work. P.V. reports personal fees from Astellas, Pfizer, Sanofi, and Biosciences,
and grants from MSD, outside the submitted work. J.R.Z. reports personal fees from MSD, Pfizer,
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Eumedica, and Correvio, and grants from MSD, outside the submitted work. L.O. reports research
grants from Pfizer and personal fees from World Health Organization South East Asia, outside the
submitted work. All other authors have no potential conflicts.
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12Table 1 – Range of estimated reproduction numbers ( ) values obtained when ranges from 10
to 40 minutes, for different assumed values of (rows) and (columns)
Average number of daily contacts in the healthcare setting ( )
5 10 15 18 20
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2 0.4-1.7 0.8-3.3 1.3-5 1.5-6 1.7-6.7
Assumed value for
2.5 0.5-2.1 1-4.2 1.6-6.3 1.9-7.5 2.1-8.3
basic reproduction
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3 0.6-2.5 1.3-5 1.9-7.5 2.3-9 2.5-10
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number in the
3.5 0.7-2.9 1.5-5.8 2.2-8.8 2.6-10.5 2.9-11.7
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community ( )
4 0.8-3.3 1.7-6.7 2.5-10 3-12 3.3-13.3
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